PROJECT SUMMARY Great progress has been made in mapping the transcriptome and some its epigenomic determinants of the adult and developing human cerebral cortex (including alterations in common psychiatric disease) through the efforts of the PsychENCODE consortium. However, next to nothing, or very little, is known about genomic regulation in brainstem monoaminergic neurons and their ascending projections into the forebrain, a circuitry critically involved in the pathophysiology of mood and psychosis spectrum disorders and substance abuse disorders, among others. The goal of our project is to construct transcriptome and epigenome (incl. 3D genome/chromosomal conformation) maps for midbrain dopaminergic neurons and for their surrounding non- neuronal cells, and to assess the relationship to known genetic risk factors for complex mental illness, including psychosis with substance abuse co-morbidity. We will apply integrative methods for functional analysis of risk genetic variation and networks, including but not limited to Bayesian network reconstruction and prediction algorithms of variant causality to identify key drivers of schizophrenia and bipolar disease pathology, and drug addiction co-morbidity. These methods will simultaneously integrate multiple different dimensions of data: DNA variation, RNA expression, chromatin accessibility, 3D structure of the genome, known pathway and gene network information in the context of clinical phenotype data. The fundamental source of data for the project comes from the current studies on human midbrain functional omics, and the CommonMinds and PsychENCODE consortia (whole genome sequencing and cortical functional omics data), the Psychiatric Genomics Consortium and the Million Veterans Project (genetic variation and disease phenotypes). The Million Veterans Project (MVP) has collected genotyping and phenotypic data from ~700,000 individuals, including a subgroup of 50,000 veterans diagnosed with SCZ and BD and a larger group of individuals diagnosed with other neuropsychiatric traits (recurrent depression, suicide and substance abuse). We will make our newly generated transcriptome and epigenome datasets from adult midbrain, as well as the network and predictive models, available to the research community in accordance with NIMH data sharing policies.